- Nov 30, 2017
- First Name
Basically in this model because each is a random variable, you'll notice for 4 states the probabilities add up to 400%. For your second question I think what you are getting at is the debate between Bayesian models and Markov models which is whether or not states/choices/outcomes should be thought of as independently and random (Markov) or if we should include some sort of historical information to define our probabilities (Bayesian). That said I put a huge asterix on all of this, I did good maths just like I did good englishes.Hey @jon.berna, will you explain this phrase to me? Not asking to be a jerk and this isn't a "gotcha" attempt, I'm here to learn.
It seems to me that confidence in the complete model relies entirely upon confidence in the accuracy of the estimations of transition probabilities. Is that correct? In other words, if the estimate of transition probability from C1 to C2 is inaccurate, the entirety of the equation is flawed. This would then be exacerbated by the potential error rate for each estimation in series as it compounds the error and variability of the equation.
1. Is that a fair assessment of the analysis above? What am I missing?
2. If so, what steps did you take, tests did you perform, behavior did you observe etc. to assure that your estimations were within an acceptable statistical error rate?